Overview

Dataset statistics

Number of variables12
Number of observations27820
Missing cells19456
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory96.0 B

Variable types

Text2
Numeric7
Categorical3

Alerts

HDI for year has 19456 (69.9%) missing valuesMissing
sex is uniformly distributedUniform
age is uniformly distributedUniform
suicides_no has 4281 (15.4%) zerosZeros
suicides/100k pop has 4281 (15.4%) zerosZeros

Reproduction

Analysis started2024-03-16 04:52:04.661409
Analysis finished2024-03-16 04:52:13.707719
Duration9.05 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size217.5 KiB
2024-03-15T22:52:14.012748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length28
Median length21
Mean length8.6248023
Min length4

Characters and Unicode

Total characters239942
Distinct characters49
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbania
2nd rowAlbania
3rd rowAlbania
4th rowAlbania
5th rowAlbania
ValueCountFrequency (%)
and 1008
 
2.9%
united 816
 
2.4%
republic 694
 
2.0%
saint 672
 
1.9%
austria 382
 
1.1%
mauritius 382
 
1.1%
netherlands 382
 
1.1%
iceland 382
 
1.1%
ecuador 372
 
1.1%
spain 372
 
1.1%
Other values (111) 29112
84.2%
2024-03-15T22:52:14.494907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 36862
15.4%
i 19394
 
8.1%
n 18220
 
7.6%
e 17874
 
7.4%
r 15174
 
6.3%
t 10848
 
4.5%
u 10558
 
4.4%
l 10082
 
4.2%
o 9220
 
3.8%
d 8594
 
3.6%
Other values (39) 83116
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 199994
83.4%
Uppercase Letter 33194
 
13.8%
Space Separator 6754
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 36862
18.4%
i 19394
9.7%
n 18220
9.1%
e 17874
8.9%
r 15174
 
7.6%
t 10848
 
5.4%
u 10558
 
5.3%
l 10082
 
5.0%
o 9220
 
4.6%
d 8594
 
4.3%
Other values (15) 43168
21.6%
Uppercase Letter
ValueCountFrequency (%)
S 4378
13.2%
B 2868
 
8.6%
A 2672
 
8.0%
C 2514
 
7.6%
G 2218
 
6.7%
R 2084
 
6.3%
K 1836
 
5.5%
P 1788
 
5.4%
U 1752
 
5.3%
I 1486
 
4.5%
Other values (13) 9598
28.9%
Space Separator
ValueCountFrequency (%)
6754
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 233188
97.2%
Common 6754
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 36862
15.8%
i 19394
 
8.3%
n 18220
 
7.8%
e 17874
 
7.7%
r 15174
 
6.5%
t 10848
 
4.7%
u 10558
 
4.5%
l 10082
 
4.3%
o 9220
 
4.0%
d 8594
 
3.7%
Other values (38) 76362
32.7%
Common
ValueCountFrequency (%)
6754
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 239942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 36862
15.4%
i 19394
 
8.1%
n 18220
 
7.6%
e 17874
 
7.4%
r 15174
 
6.3%
t 10848
 
4.5%
u 10558
 
4.4%
l 10082
 
4.2%
o 9220
 
3.8%
d 8594
 
3.6%
Other values (39) 83116
34.6%

year
Real number (ℝ)

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.2584
Minimum1985
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size217.5 KiB
2024-03-15T22:52:14.685811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1987
Q11995
median2002
Q32008
95-th percentile2014
Maximum2016
Range31
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.469055
Coefficient of variation (CV)0.0042318649
Kurtosis-1.0517491
Mean2001.2584
Median Absolute Deviation (MAD)7
Skewness-0.1602413
Sum55675008
Variance71.724893
MonotonicityNot monotonic
2024-03-15T22:52:14.905133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2009 1068
 
3.8%
2010 1056
 
3.8%
2001 1056
 
3.8%
2002 1032
 
3.7%
2000 1032
 
3.7%
2011 1032
 
3.7%
2007 1032
 
3.7%
2003 1032
 
3.7%
2008 1020
 
3.7%
2006 1020
 
3.7%
Other values (22) 17440
62.7%
ValueCountFrequency (%)
1985 576
2.1%
1986 576
2.1%
1987 648
2.3%
1988 588
2.1%
1989 624
2.2%
1990 768
2.8%
1991 768
2.8%
1992 780
2.8%
1993 780
2.8%
1994 816
2.9%
ValueCountFrequency (%)
2016 160
 
0.6%
2015 744
2.7%
2014 936
3.4%
2013 960
3.5%
2012 972
3.5%
2011 1032
3.7%
2010 1056
3.8%
2009 1068
3.8%
2008 1020
3.7%
2007 1032
3.7%

sex
Categorical

UNIFORM 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size217.5 KiB
male
13910 
female
13910 

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters139100
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowfemale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
male 13910
50.0%
female 13910
50.0%

Length

2024-03-15T22:52:15.090644image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T22:52:15.249498image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
male 13910
50.0%
female 13910
50.0%

Most occurring characters

ValueCountFrequency (%)
e 41730
30.0%
m 27820
20.0%
a 27820
20.0%
l 27820
20.0%
f 13910
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139100
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 41730
30.0%
m 27820
20.0%
a 27820
20.0%
l 27820
20.0%
f 13910
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 139100
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 41730
30.0%
m 27820
20.0%
a 27820
20.0%
l 27820
20.0%
f 13910
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 41730
30.0%
m 27820
20.0%
a 27820
20.0%
l 27820
20.0%
f 13910
 
10.0%

age
Categorical

UNIFORM 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size217.5 KiB
15-24
4642 
35-54
4642 
75+
4642 
25-34
4642 
55-74
4642 

Length

Max length5
Median length5
Mean length4.5005751
Min length3

Characters and Unicode

Total characters125206
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15-24
2nd row35-54
3rd row15-24
4th row75+
5th row25-34

Common Values

ValueCountFrequency (%)
15-24 4642
16.7%
35-54 4642
16.7%
75+ 4642
16.7%
25-34 4642
16.7%
55-74 4642
16.7%
5-14 4610
16.6%

Length

2024-03-15T22:52:15.407340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T22:52:15.792536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
15-24 4642
16.7%
35-54 4642
16.7%
75 4642
16.7%
25-34 4642
16.7%
55-74 4642
16.7%
5-14 4610
16.6%

Most occurring characters

ValueCountFrequency (%)
5 37104
29.6%
- 23178
18.5%
4 23178
18.5%
2 9284
 
7.4%
3 9284
 
7.4%
7 9284
 
7.4%
1 9252
 
7.4%
+ 4642
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97386
77.8%
Dash Punctuation 23178
 
18.5%
Math Symbol 4642
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 37104
38.1%
4 23178
23.8%
2 9284
 
9.5%
3 9284
 
9.5%
7 9284
 
9.5%
1 9252
 
9.5%
Dash Punctuation
ValueCountFrequency (%)
- 23178
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 125206
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 37104
29.6%
- 23178
18.5%
4 23178
18.5%
2 9284
 
7.4%
3 9284
 
7.4%
7 9284
 
7.4%
1 9252
 
7.4%
+ 4642
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 37104
29.6%
- 23178
18.5%
4 23178
18.5%
2 9284
 
7.4%
3 9284
 
7.4%
7 9284
 
7.4%
1 9252
 
7.4%
+ 4642
 
3.7%

suicides_no
Real number (ℝ)

ZEROS 

Distinct2084
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.57441
Minimum0
Maximum22338
Zeros4281
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size217.5 KiB
2024-03-15T22:52:15.979575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median25
Q3131
95-th percentile1050.05
Maximum22338
Range22338
Interquartile range (IQR)128

Descriptive statistics

Standard deviation902.04792
Coefficient of variation (CV)3.7186442
Kurtosis157.16884
Mean242.57441
Median Absolute Deviation (MAD)25
Skewness10.35291
Sum6748420
Variance813690.44
MonotonicityNot monotonic
2024-03-15T22:52:16.178013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4281
 
15.4%
1 1539
 
5.5%
2 1102
 
4.0%
3 867
 
3.1%
4 696
 
2.5%
5 538
 
1.9%
6 467
 
1.7%
7 429
 
1.5%
8 365
 
1.3%
9 349
 
1.3%
Other values (2074) 17187
61.8%
ValueCountFrequency (%)
0 4281
15.4%
1 1539
 
5.5%
2 1102
 
4.0%
3 867
 
3.1%
4 696
 
2.5%
5 538
 
1.9%
6 467
 
1.7%
7 429
 
1.5%
8 365
 
1.3%
9 349
 
1.3%
ValueCountFrequency (%)
22338 1
< 0.1%
21706 1
< 0.1%
21262 1
< 0.1%
21063 1
< 0.1%
20705 1
< 0.1%
20562 1
< 0.1%
20256 1
< 0.1%
20119 1
< 0.1%
18973 1
< 0.1%
18681 1
< 0.1%

population
Real number (ℝ)

Distinct25564
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1844793.6
Minimum278
Maximum43805214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size217.5 KiB
2024-03-15T22:52:16.358896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile7195.6
Q197498.5
median430150
Q31486143.2
95-th percentile8850239.6
Maximum43805214
Range43804936
Interquartile range (IQR)1388644.8

Descriptive statistics

Standard deviation3911779.4
Coefficient of variation (CV)2.1204429
Kurtosis27.407176
Mean1844793.6
Median Absolute Deviation (MAD)398150
Skewness4.4594144
Sum5.1322158 × 1010
Variance1.5302018 × 1013
MonotonicityNot monotonic
2024-03-15T22:52:16.560925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24000 20
 
0.1%
26900 13
 
< 0.1%
20700 12
 
< 0.1%
22000 12
 
< 0.1%
4900 11
 
< 0.1%
20500 10
 
< 0.1%
1000 10
 
< 0.1%
9000 10
 
< 0.1%
21700 10
 
< 0.1%
28600 9
 
< 0.1%
Other values (25554) 27703
99.6%
ValueCountFrequency (%)
278 2
< 0.1%
286 1
< 0.1%
287 1
< 0.1%
290 1
< 0.1%
291 1
< 0.1%
293 1
< 0.1%
294 1
< 0.1%
297 1
< 0.1%
302 1
< 0.1%
304 1
< 0.1%
ValueCountFrequency (%)
43805214 1
< 0.1%
43607902 1
< 0.1%
43509335 1
< 0.1%
43240905 1
< 0.1%
43139910 1
< 0.1%
43002471 1
< 0.1%
42997878 1
< 0.1%
42992076 1
< 0.1%
42957716 1
< 0.1%
42932194 1
< 0.1%

suicides/100k pop
Real number (ℝ)

ZEROS 

Distinct5298
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.816097
Minimum0
Maximum224.97
Zeros4281
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size217.5 KiB
2024-03-15T22:52:16.747702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.92
median5.99
Q316.62
95-th percentile50.5305
Maximum224.97
Range224.97
Interquartile range (IQR)15.7

Descriptive statistics

Standard deviation18.961511
Coefficient of variation (CV)1.4795074
Kurtosis12.165745
Mean12.816097
Median Absolute Deviation (MAD)5.82
Skewness2.9634145
Sum356543.83
Variance359.5389
MonotonicityNot monotonic
2024-03-15T22:52:16.932987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4281
 
15.4%
0.29 72
 
0.3%
0.32 69
 
0.2%
0.34 55
 
0.2%
0.37 52
 
0.2%
0.33 49
 
0.2%
0.3 48
 
0.2%
0.41 47
 
0.2%
0.31 46
 
0.2%
0.22 46
 
0.2%
Other values (5288) 23055
82.9%
ValueCountFrequency (%)
0 4281
15.4%
0.02 5
 
< 0.1%
0.03 8
 
< 0.1%
0.04 14
 
0.1%
0.05 10
 
< 0.1%
0.06 16
 
0.1%
0.07 9
 
< 0.1%
0.08 27
 
0.1%
0.09 10
 
< 0.1%
0.1 19
 
0.1%
ValueCountFrequency (%)
224.97 1
< 0.1%
204.92 1
< 0.1%
187.06 1
< 0.1%
185.37 1
< 0.1%
182.32 1
< 0.1%
177.61 1
< 0.1%
177.57 1
< 0.1%
176.91 1
< 0.1%
176.33 1
< 0.1%
176.26 1
< 0.1%
Distinct2321
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size217.5 KiB
2024-03-15T22:52:17.229925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length32
Median length25
Mean length12.624802
Min length8

Characters and Unicode

Total characters351222
Distinct characters59
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbania1987
2nd rowAlbania1987
3rd rowAlbania1987
4th rowAlbania1987
5th rowAlbania1987
ValueCountFrequency (%)
and 1008
 
2.9%
united 816
 
2.4%
saint 672
 
1.9%
of 372
 
1.1%
republic 372
 
1.1%
puerto 372
 
1.1%
costa 360
 
1.0%
new 348
 
1.0%
trinidad 324
 
0.9%
antigua 324
 
0.9%
Other values (2332) 29606
85.6%
2024-03-15T22:52:17.750470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 36862
 
10.5%
0 29332
 
8.4%
9 22980
 
6.5%
1 20368
 
5.8%
i 19394
 
5.5%
2 18952
 
5.4%
n 18220
 
5.2%
e 17874
 
5.1%
r 15174
 
4.3%
t 10848
 
3.1%
Other values (49) 141218
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 199994
56.9%
Decimal Number 111280
31.7%
Uppercase Letter 33194
 
9.5%
Space Separator 6754
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 36862
18.4%
i 19394
9.7%
n 18220
9.1%
e 17874
8.9%
r 15174
 
7.6%
t 10848
 
5.4%
u 10558
 
5.3%
l 10082
 
5.0%
o 9220
 
4.6%
d 8594
 
4.3%
Other values (15) 43168
21.6%
Uppercase Letter
ValueCountFrequency (%)
S 4378
13.2%
B 2868
 
8.6%
A 2672
 
8.0%
C 2514
 
7.6%
G 2218
 
6.7%
R 2084
 
6.3%
K 1836
 
5.5%
P 1788
 
5.4%
U 1752
 
5.3%
I 1486
 
4.5%
Other values (13) 9598
28.9%
Decimal Number
ValueCountFrequency (%)
0 29332
26.4%
9 22980
20.7%
1 20368
18.3%
2 18952
17.0%
8 5568
 
5.0%
5 3264
 
2.9%
3 2772
 
2.5%
4 2760
 
2.5%
6 2680
 
2.4%
7 2604
 
2.3%
Space Separator
ValueCountFrequency (%)
6754
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 233188
66.4%
Common 118034
33.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 36862
15.8%
i 19394
 
8.3%
n 18220
 
7.8%
e 17874
 
7.7%
r 15174
 
6.5%
t 10848
 
4.7%
u 10558
 
4.5%
l 10082
 
4.3%
o 9220
 
4.0%
d 8594
 
3.7%
Other values (38) 76362
32.7%
Common
ValueCountFrequency (%)
0 29332
24.9%
9 22980
19.5%
1 20368
17.3%
2 18952
16.1%
6754
 
5.7%
8 5568
 
4.7%
5 3264
 
2.8%
3 2772
 
2.3%
4 2760
 
2.3%
6 2680
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 36862
 
10.5%
0 29332
 
8.4%
9 22980
 
6.5%
1 20368
 
5.8%
i 19394
 
5.5%
2 18952
 
5.4%
n 18220
 
5.2%
e 17874
 
5.1%
r 15174
 
4.3%
t 10848
 
3.1%
Other values (49) 141218
40.2%

HDI for year
Real number (ℝ)

MISSING 

Distinct305
Distinct (%)3.6%
Missing19456
Missing (%)69.9%
Infinite0
Infinite (%)0.0%
Mean0.77660115
Minimum0.483
Maximum0.944
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size217.5 KiB
2024-03-15T22:52:17.966318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.483
5-th percentile0.619
Q10.713
median0.779
Q30.855
95-th percentile0.912
Maximum0.944
Range0.461
Interquartile range (IQR)0.142

Descriptive statistics

Standard deviation0.093366709
Coefficient of variation (CV)0.12022479
Kurtosis-0.64791393
Mean0.77660115
Median Absolute Deviation (MAD)0.071
Skewness-0.30087745
Sum6495.492
Variance0.0087173423
MonotonicityNot monotonic
2024-03-15T22:52:18.154911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.713 84
 
0.3%
0.772 84
 
0.3%
0.888 84
 
0.3%
0.761 72
 
0.3%
0.909 72
 
0.3%
0.83 72
 
0.3%
0.756 72
 
0.3%
0.827 72
 
0.3%
0.793 72
 
0.3%
0.785 60
 
0.2%
Other values (295) 7620
 
27.4%
(Missing) 19456
69.9%
ValueCountFrequency (%)
0.483 12
< 0.1%
0.513 12
< 0.1%
0.522 12
< 0.1%
0.539 12
< 0.1%
0.542 12
< 0.1%
0.552 12
< 0.1%
0.562 12
< 0.1%
0.564 12
< 0.1%
0.566 12
< 0.1%
0.572 12
< 0.1%
ValueCountFrequency (%)
0.944 12
< 0.1%
0.942 24
0.1%
0.941 12
< 0.1%
0.94 12
< 0.1%
0.935 12
< 0.1%
0.933 12
< 0.1%
0.932 12
< 0.1%
0.931 12
< 0.1%
0.93 24
0.1%
0.928 12
< 0.1%

gdp_for_year
Real number (ℝ)

Distinct2321
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4558097 × 1011
Minimum46919625
Maximum1.8120714 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size217.5 KiB
2024-03-15T22:52:18.343158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum46919625
5-th percentile7.2786059 × 108
Q18.9853528 × 109
median4.8114688 × 1010
Q32.6020243 × 1011
95-th percentile2.1231309 × 1012
Maximum1.8120714 × 1013
Range1.8120667 × 1013
Interquartile range (IQR)2.5121708 × 1011

Descriptive statistics

Standard deviation1.45361 × 1012
Coefficient of variation (CV)3.2622802
Kurtosis64.233625
Mean4.4558097 × 1011
Median Absolute Deviation (MAD)4.6739084 × 1010
Skewness7.233755
Sum1.2396063 × 1016
Variance2.112982 × 1024
MonotonicityNot monotonic
2024-03-15T22:52:18.539663image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2156624900 12
 
< 0.1%
9.604564503 × 101012
 
< 0.1%
2.71166356 × 101012
 
< 0.1%
2.94402876 × 101012
 
< 0.1%
3.46862243 × 101012
 
< 0.1%
4.04297344 × 101012
 
< 0.1%
4.5599994 × 101012
 
< 0.1%
4.99214644 × 101012
 
< 0.1%
5.43157225 × 101012
 
< 0.1%
2966234106 12
 
< 0.1%
Other values (2311) 27700
99.6%
ValueCountFrequency (%)
46919625 12
< 0.1%
47515189 12
< 0.1%
47737955 12
< 0.1%
54832578 12
< 0.1%
56338028 12
< 0.1%
63101272 12
< 0.1%
65334841 12
< 0.1%
66515377 12
< 0.1%
67254174 12
< 0.1%
67537480 12
< 0.1%
ValueCountFrequency (%)
1.8120714 × 101312
< 0.1%
1.7427609 × 101312
< 0.1%
1.6691517 × 101312
< 0.1%
1.6155255 × 101312
< 0.1%
1.5517926 × 101312
< 0.1%
1.4964372 × 101312
< 0.1%
1.4718582 × 101312
< 0.1%
1.4477635 × 101312
< 0.1%
1.4418739 × 101312
< 0.1%
1.3855888 × 101312
< 0.1%

gdp_per_capita
Real number (ℝ)

Distinct2233
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16866.464
Minimum251
Maximum126352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size217.5 KiB
2024-03-15T22:52:18.751276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum251
5-th percentile935
Q13447
median9372
Q324874
95-th percentile54294
Maximum126352
Range126101
Interquartile range (IQR)21427

Descriptive statistics

Standard deviation18887.576
Coefficient of variation (CV)1.1198302
Kurtosis4.937758
Mean16866.464
Median Absolute Deviation (MAD)7334
Skewness1.96347
Sum4.6922504 × 108
Variance3.5674054 × 108
MonotonicityNot monotonic
2024-03-15T22:52:18.957102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2303 36
 
0.1%
1299 36
 
0.1%
4104 36
 
0.1%
1698 24
 
0.1%
939 24
 
0.1%
3639 24
 
0.1%
4046 24
 
0.1%
21027 24
 
0.1%
4115 24
 
0.1%
2947 24
 
0.1%
Other values (2223) 27544
99.0%
ValueCountFrequency (%)
251 12
< 0.1%
291 12
< 0.1%
313 12
< 0.1%
345 12
< 0.1%
357 12
< 0.1%
359 12
< 0.1%
385 12
< 0.1%
387 12
< 0.1%
398 12
< 0.1%
424 12
< 0.1%
ValueCountFrequency (%)
126352 12
< 0.1%
122729 12
< 0.1%
121315 12
< 0.1%
120423 12
< 0.1%
113120 12
< 0.1%
112581 12
< 0.1%
111328 12
< 0.1%
109804 12
< 0.1%
109483 12
< 0.1%
108408 12
< 0.1%

generation
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size217.5 KiB
Generation X
6408 
Silent
6364 
Millenials
5844 
Boomers
4990 
G.I. Generation
2744 

Length

Max length15
Median length12
Mean length9.6063983
Min length6

Characters and Unicode

Total characters267250
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGeneration X
2nd rowSilent
3rd rowGeneration X
4th rowG.I. Generation
5th rowBoomers

Common Values

ValueCountFrequency (%)
Generation X 6408
23.0%
Silent 6364
22.9%
Millenials 5844
21.0%
Boomers 4990
17.9%
G.I. Generation 2744
9.9%
Generation Z 1470
 
5.3%

Length

2024-03-15T22:52:19.139486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T22:52:19.296168image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
generation 10622
27.6%
x 6408
16.7%
silent 6364
16.6%
millenials 5844
15.2%
boomers 4990
13.0%
g.i 2744
 
7.1%
z 1470
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 38442
14.4%
n 33452
12.5%
i 28674
10.7%
l 23896
8.9%
o 20602
7.7%
t 16986
 
6.4%
a 16466
 
6.2%
r 15612
 
5.8%
G 13366
 
5.0%
s 10834
 
4.1%
Other values (9) 48920
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 209954
78.6%
Uppercase Letter 41186
 
15.4%
Space Separator 10622
 
4.0%
Other Punctuation 5488
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 38442
18.3%
n 33452
15.9%
i 28674
13.7%
l 23896
11.4%
o 20602
9.8%
t 16986
8.1%
a 16466
7.8%
r 15612
7.4%
s 10834
 
5.2%
m 4990
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
G 13366
32.5%
X 6408
15.6%
S 6364
15.5%
M 5844
14.2%
B 4990
 
12.1%
I 2744
 
6.7%
Z 1470
 
3.6%
Space Separator
ValueCountFrequency (%)
10622
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5488
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 251140
94.0%
Common 16110
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 38442
15.3%
n 33452
13.3%
i 28674
11.4%
l 23896
9.5%
o 20602
8.2%
t 16986
6.8%
a 16466
6.6%
r 15612
6.2%
G 13366
 
5.3%
s 10834
 
4.3%
Other values (7) 32810
13.1%
Common
ValueCountFrequency (%)
10622
65.9%
. 5488
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 267250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 38442
14.4%
n 33452
12.5%
i 28674
10.7%
l 23896
8.9%
o 20602
7.7%
t 16986
 
6.4%
a 16466
 
6.2%
r 15612
 
5.8%
G 13366
 
5.0%
s 10834
 
4.1%
Other values (9) 48920
18.3%

Interactions

2024-03-15T22:52:12.177020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:06.016061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:07.448936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:08.382162image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:09.292612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:10.178687image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:11.116888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:12.318927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:06.196343image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:07.593296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:08.517040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:09.425515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:10.313275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:11.265411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:12.447776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:06.538493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:07.719969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:08.639808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:09.540707image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:10.441920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:11.417725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:12.580561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:06.719513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:07.847094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:08.761379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:09.664141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:10.574586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:11.578815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:12.726919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:06.886253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:07.965690image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:08.882598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:09.780011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:10.706808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:11.730309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:12.857493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:07.062381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:08.104365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:09.015271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:09.912752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:10.832305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:11.863394image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:13.001704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:07.256752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:08.245344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:09.161793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:10.048191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:10.971539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-03-15T22:52:12.023649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-03-15T22:52:13.190195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T22:52:13.500090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

countryyearsexagesuicides_nopopulationsuicides/100k popcountry-yearHDI for yeargdp_for_yeargdp_per_capitageneration
0Albania1987male15-24213129006.71Albania1987NaN2.156625e+09796.0Generation X
1Albania1987male35-54163080005.19Albania1987NaN2.156625e+09796.0Silent
2Albania1987female15-24142897004.83Albania1987NaN2.156625e+09796.0Generation X
3Albania1987male75+1218004.59Albania1987NaN2.156625e+09796.0G.I. Generation
4Albania1987male25-3492743003.28Albania1987NaN2.156625e+09796.0Boomers
5Albania1987female75+1356002.81Albania1987NaN2.156625e+09796.0G.I. Generation
6Albania1987female35-5462788002.15Albania1987NaN2.156625e+09796.0Silent
7Albania1987female25-3442572001.56Albania1987NaN2.156625e+09796.0Boomers
8Albania1987male55-7411375000.73Albania1987NaN2.156625e+09796.0G.I. Generation
9Albania1987female5-1403110000.00Albania1987NaN2.156625e+09796.0Generation X
countryyearsexagesuicides_nopopulationsuicides/100k popcountry-yearHDI for yeargdp_for_yeargdp_per_capitageneration
27810Uzbekistan2014female15-24347299281711.59Uzbekistan20140.6756.306708e+102309.0Millenials
27811Uzbekistan2014male55-74144127111111.33Uzbekistan20140.6756.306708e+102309.0Boomers
27812Uzbekistan2014male15-24347312690511.10Uzbekistan20140.6756.306708e+102309.0Millenials
27813Uzbekistan2014male75+172249957.56Uzbekistan20140.6756.306708e+102309.0Silent
27814Uzbekistan2014female25-3416227352385.92Uzbekistan20140.6756.306708e+102309.0Millenials
27815Uzbekistan2014female35-5410736208332.96Uzbekistan20140.6756.306708e+102309.0Generation X
27816Uzbekistan2014female75+93484652.58Uzbekistan20140.6756.306708e+102309.0Silent
27817Uzbekistan2014male5-146027621582.17Uzbekistan20140.6756.306708e+102309.0Generation Z
27818Uzbekistan2014female5-144426316001.67Uzbekistan20140.6756.306708e+102309.0Generation Z
27819Uzbekistan2014female55-742114389351.46Uzbekistan20140.6756.306708e+102309.0Boomers